docstr: ivy.Array instance method variant of ivy.randint. This method simply
  wraps the function, and so the docstring for ivy.randint also applies
  to this method with minimal changes.
  
  Parameters
  ----------
  self
      Lowest integer that can be drawn from the distribution.
  high
      One above the highest integer that can be drawn from the distribution.
  shape
      If the given shape is, e.g ``(m, n, k)``, then ``m * n * k`` samples
      are drawn. Can only be specified when ``low`` and ``high`` are numeric
      values, else exception will be raised.
      Default is ``None``, where a single value is returned.
  device
      device on which to create the array 'cuda:0', 'cuda:1', 'cpu' etc.
      (Default value = None).
  dtype
       output array data type. If ``dtype`` is ``None``, the output array data
       type will be the default integer data type. Default ``None``
  seed
      A python integer. Used to create a random seed distribution
  out
      optional output array, for writing the result to. It must have a shape
      that the inputs broadcast to.
  
  Returns
  -------
  ret
      Returns an array with the given shape filled with integers from
      the uniform distribution in the “half-open” interval [low, high)
  
  Examples
  --------
  >>> x = ivy.array([[1, 2], [0, 5]])
  >>> x.randint(10)
  ivy.array([[1, 5],
             [9, 7]])
  
  >>> x.randint(8, device='cpu')
  ivy.array([[6, 5],
             [0, 5]])
  
  >>> x.randint(9, dtype='int8')
  ivy.array([[1, 2],
             [7, 7]])
  
  >>> x.randint(14, device='cpu', dtype='int16')
  ivy.array([[6, 5],
             [0, 5]])
  
  >>> z = ivy.ones((2,2))
  >>> x.randint(16, device='cpu', dtype='int64', out=z)
  ivy.array([[1, 2],
             [7, 7]])
  
  >>> x = ivy.array([1, 2, 3])
  >>> y = ivy.array([23, 25, 98])
  >>> x.randint(y)
  ivy.array([ 5, 14, 18])
  
  >>> x.randint(y, device='cpu')
  ivy.array([20, 13, 46])
  
  >>> x.randint(y, dtype='int32')
  ivy.array([ 9, 18, 33])
  
  >>> x.randint(y, device='cpu', dtype='int16')
  ivy.array([ 9, 20, 85])
  
  >>> z = ivy.ones((3,))
  >>> x.randint(y, device='cpu', dtype='int64', out=z)
  ivy.array([20, 13, 46])
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code: def randint(
      self: ivy.Array,
      high: Union[int, ivy.Array, ivy.NativeArray],
      /,
      *,
      shape: Optional[Union[ivy.Shape, ivy.NativeShape]] = None,
      device: Optional[Union[ivy.Device, ivy.NativeDevice]] = None,
      dtype: Optional[Union[ivy.Dtype, ivy.NativeDtype]] = None,
      seed: Optional[int] = None,
      out: Optional[ivy.Array] = None,
  ) -> ivy.Array:
      """ivy.Array instance method variant of ivy.randint. This method simply
      wraps the function, and so the docstring for ivy.randint also applies
      to this method with minimal changes.
  
      Parameters
      ----------
      self
          Lowest integer that can be drawn from the distribution.
      high
          One above the highest integer that can be drawn from the distribution.
      shape
          If the given shape is, e.g ``(m, n, k)``, then ``m * n * k`` samples
          are drawn. Can only be specified when ``low`` and ``high`` are numeric
          values, else exception will be raised.
          Default is ``None``, where a single value is returned.
      device
          device on which to create the array 'cuda:0', 'cuda:1', 'cpu' etc.
          (Default value = None).
      dtype
           output array data type. If ``dtype`` is ``None``, the output array data
           type will be the default integer data type. Default ``None``
      seed
          A python integer. Used to create a random seed distribution
      out
          optional output array, for writing the result to. It must have a shape
          that the inputs broadcast to.
  
      Returns
      -------
      ret
          Returns an array with the given shape filled with integers from
          the uniform distribution in the “half-open” interval [low, high)
  
      Examples
      --------
      >>> x = ivy.array([[1, 2], [0, 5]])
      >>> x.randint(10)
      ivy.array([[1, 5],
                 [9, 7]])
  
      >>> x.randint(8, device='cpu')
      ivy.array([[6, 5],
                 [0, 5]])
  
      >>> x.randint(9, dtype='int8')
      ivy.array([[1, 2],
                 [7, 7]])
  
      >>> x.randint(14, device='cpu', dtype='int16')
      ivy.array([[6, 5],
                 [0, 5]])
  
      >>> z = ivy.ones((2,2))
      >>> x.randint(16, device='cpu', dtype='int64', out=z)
      ivy.array([[1, 2],
                 [7, 7]])
  
      >>> x = ivy.array([1, 2, 3])
      >>> y = ivy.array([23, 25, 98])
      >>> x.randint(y)
      ivy.array([ 5, 14, 18])
  
      >>> x.randint(y, device='cpu')
      ivy.array([20, 13, 46])
  
      >>> x.randint(y, dtype='int32')
      ivy.array([ 9, 18, 33])
  
      >>> x.randint(y, device='cpu', dtype='int16')
      ivy.array([ 9, 20, 85])
  
      >>> z = ivy.ones((3,))
      >>> x.randint(y, device='cpu', dtype='int64', out=z)
      ivy.array([20, 13, 46])
      """
      return ivy.randint(
          self._data,
          high,
          shape=shape,
          device=device,
          dtype=dtype,
          seed=seed,
          out=out,
      )
  
file_path: ivy/ivy/data_classes/array/random.py
imports: import abc
  from typing import Optional, Union
  import ivy
label_desc: Use image node_id 4 for calling the _ArrayWithRandom obj's underlying member method code with example usage: obj.randint() and returns: ivy